48-hour access to this article
USD $30.00
USD $30.00
Whole-genome DNA methylation analysis of the sperm in relation to bull fertility
in ReproductionIn brief
Bull fertility is an important economic trait, this study identified some DNA methylation biomarkers that are associated with bull fertility.
Abstract
Subfertile bulls may cause huge economic losses in dairy production since their semen could be used to inseminate thousands of cows by artificial insemination. This study adopted whole-genome enzymatic methyl sequencing and aimed to identify candidate DNA methylation markers in bovine sperm that correlate with bull fertility. Twelve bulls were selected (high bull fertility = 6; low bull fertility = 6) based on the industry’s internally used Bull Fertility Index. After sequencing, a total of 450 CpG had a DNA methylation difference higher than 20% (q < 0.01) had been screened. The 16 most significant differentially methylated regions (DMRs) were identified using a 10% methylation difference cut-off (q < 5.88 × 10−16). Interestingly, most of the differentially methylated cytosines (DMCs) and DMRs were distributed on the X and Y chromosomes, demonstrating that the sex chromosomes play essential roles in bull fertility. Additionally, the functional classification showed that the beta-defensin family, zinc finger protein family, and olfactory and taste receptors could be clustered. Moreover, the enriched G protein-coupled receptors such as neurotransmitter receptors, taste receptors, olfactory receptors, and ion channels indicated that the acrosome reaction and capacitation processes are pivotal for bull fertility. In conclusion, this study identified the sperm-derived bull fertility-associated DMRs and DMCs at the whole genome level, which could complement and integrate into the existing genetic evaluation methods, increasing our decisive capacity to select good bulls and explain bull fertility better in the future.
Supplementary Materials
- Supplementary file1 Sheet1. Information about the significant differentially methylated cytosines in the CG context (q<0.01 and methylation difference higher than 20%). Sheet2. Gene list used for gene ontology analysis covered at least one DMC (q<0.05). Sheet3.The C-T conversion rate calculated via imprinting genes. Sheet4.The overlapping between the DMCs and the SNVs.Sheet5: The information including the age and the bull fertility index of the samples.
- Supplementary file 2 Biological process connected pathways that were performed via ClueGO. From the Biological process ontology sources, related terms with similar associated genes can be fused to reduce redundancy. The bigger the node size is, the more significant the terms/pathways are. Each color represents a term/pathway, and all the terms p value<0.05.
- Supplementary file 3 Cell component connected pathways that were performed via ClueGO, From the cell component ontology sources used, the related terms which share similar associated genes can be fused to reduce redundancy. The bigger the node size is, the more significant the terms/pathways are. Each color represents a term/pathway—all the terms p value<0.05.
- Supplementary file 4 Molecular function connected pathways that were performed via ClueGO, From the molecular function ontology sources used, the related terms which share similar associated genes can be fused to reduce redundancy. The bigger the node size is, the more significant the terms/pathways are. Each color represents a term/pathway—all the terms p value<0.05.
- Supplementary file 5 Sheet 1. List of the genes containing at least one DMR (q<0.05). Sheet 2. The low classification stringency was adopted for the functional classification of the genes containing at least one DMRs (q<0.05). (Similarity term overlap=4, similarity threshold=0.03, initial group membership=3, final group membership=3, multiple linkage threshold=0.50)
- Supplementary file 6 21 genes overlapped between other bull fertility study and our finding (genes that cover DMC)
- Supplementary file 7
Reproduction is committed to supporting researchers in demonstrating the impact of their articles published in the journal.
The two types of article metrics we measure are (i) more traditional full-text views and pdf downloads, and (ii) Altmetric data, which shows the wider impact of articles in a range of non-traditional sources, such as social media.
More information is on the Reasons to publish page.
| Sept 2018 onwards | Past Year | Past 30 Days | |
|---|---|---|---|
| Full Text Views | 85 | 85 | 31 |
| PDF Downloads | 133 | 133 | 49 |
Online ISSN: 1741-7899
Print ISSN: 1470-1626
Strengthening biomedical communities
to advance science and health
CONTACT US
Bioscientifica Ltd | Starling House | 1600 Bristol Parkway North | Bristol BS34 8YU | UK
Bioscientifica Ltd | Registered in England no 3190519
Powered by PubFactory